DP-203 Develop data processing Practice Question
You are troubleshooting a slow-running Azure Data Factory pipeline that copies data from an Azure SQL Database to ADLS Gen2. The pipeline uses a copy activity with the default settings. The source table has 10 million rows. Which optimization should you apply first?
Answer choices
Why each option matters
Answer the question above first, then reveal the full breakdown to understand why each option is right or wrong.
Correct answer & explanation
✓
Enable staged copy using an Azure Blob Storage staging location.
Enabling staged copy via Azure Blob Storage improves performance for large datasets by allowing the copy activity to use blob storage as an intermediate staging location, reducing load on source and destination. Option A (parallel copies) may help but default auto parallelism already handles it. Option B (mapping data flow) is heavier and not needed for simple copy. Option C (increase DIU) is a more expensive option and should not be the first optimization.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✗
Set the 'parallel copies' property to 10.
Why it's wrong here
Parallel copies are automatically tuned; manual setting may not yield significant improvement.
- ✗
Replace the copy activity with a mapping data flow.
Why it's wrong here
Data flows are for transformations, not simple copies; they add overhead.
- ✗
Increase the data integration unit (DIU) to maximum.
Why it's wrong here
DIU increase costs more and may not address the bottleneck from SQL DB.
- ✓
Enable staged copy using an Azure Blob Storage staging location.
Why this is correct
Staging allows data to be transferred via Blob Storage, which improves throughput for SQL to ADLS copies.
Quick reference
Azure Blob Storage Tier Comparison
| Tier | Storage Cost | Retrieval Cost | Latency | Use Case |
|---|---|---|---|---|
| Hot | Highest | Lowest | Immediate | Active data, frequent reads |
| Cool | Lower | Higher | Immediate | Data accessed < once / month |
| Cold | Lower still | Higher | Immediate | Data accessed < once / quarter |
| Archive | Lowest | Highest + rehydration delay | Hours | Long-term compliance retention |
Go deeper
Related to this question
Learn chapter
Introduction to Azure Data Engineering
Key term
Azure Data Factory
Azure Data Factory is a cloud-based data integration service that lets you create, schedule, and orchestrate data pipelines to move and transform data from various sources to destinations.
Key term
Data Transformation Pipelines
Data transformation pipelines are automated sequences of steps that take raw data from a source, clean and reshape it into a usable format, and then load it into a destination for analysis or storage.
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Written by Johnson Ajibi, MSc IT Security
Senior Network & Security Engineer · founder of Courseiva
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